图 书 馆
贝叶斯定理中机械模型的作用The role of mechanistic models in Bayesian inference |
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| 课程网址: | http://videolectures.net/bark08_cornford_trommibi/ |
| 主讲教师: | Dan Cornford |
| 开课单位: | 阿斯顿大学 |
| 开课时间: | 2008-10-09 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | I'll outline the role of mechanistic models, or simulators, in defining priors in a Bayesian inference setting. In particular I will focus on two main cases: 1) where process based understanding of the system allows us to construct a stochastic simulator for the system - which translates to inference in stochastic processes; 2) where an existing (typically) deterministic mechanistic model exists - which we can then emulate and treat 'correctly' in a Bayesian manner. I will pay special attention to the relation between the simulator and reality, since it is reality that typically is sampled to generate the observations used for inference in the model. I will outline ideas from emulation, and show the challenges I think remain to be solved. This is joint work with lots of people: Alexis Boukouvalas, Yuan Shen, Michael Vrettas, Manfred Opper and many others in the MUCM project. |
| 关 键 词: | 贝叶斯定理; 机械模型; 概率论 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-12-24：lxf |
| 阅读次数: | 54 |